Is variety a spice of (an active) life?: Perceived variety, exercise behaviour, and the mediating role of autonomous motivation
Bibliographic record
Abstract
Beyond satisfaction of the three basic psychological needs (i.e., for competence, autonomy and relatedness) embedded within Self-Determination Theory, additional psychosocial experiences may exist and should be explored to better understand exercise motivation and behaviour. One such experience is variety, which refers to the pursuit and experience of novel and/or alternating activities, behaviours, and opportunities in one’s social environment. In this study, we examined whether perceived variety in exercise prospectively predicts unique variance in exercise behaviour when examined alongside satisfaction of the needs for competence, relatedness, and autonomy, through the mediating role of autonomous and controlled motivation. A convenience sample of community adults (N = 363) completed online questionnaires twice over a six-week period. The results of structural equation modelling showed perceived variety (β = .058, p < .01) and satisfaction of the needs for competence (β = .080, p < .05) and relatedness (β = .068, p < .01) to be unique indirect positive predictors of exercise behaviour (through autonomous motivation) six weeks later. In addition, satisfaction of the need for autonomy (β = -.203, p < .01) was found to negatively predict controlled motivation. Perceived variety in exercise complemented satisfaction of the needs for competence, relatedness, and autonomy in predicting motivation and (indirectly) exercise behaviour, and may act as a salient mechanism in the prediction of autonomous motivation and behaviour in exercise settings. The results of this study provide insight into how/why perceived variety in exercise relates to exercise behaviour and lend weight to Sheldon’s (2011) contention that satisfaction of the needs for competence, relatedness, and autonomy, may be an incomplete subset of the types of positive experiences that are involved in supporting autonomous motivation. Acknowledgments: This research was supported by a graduate scholarship awarded to Ben Sylvester by the Social Sciences and Humanities Research Council of Canada, as well as a career investigator award from the Michael Smith Foundation for Health Research awarded to Mark Beauchamp.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".